用滑动局部地图与历史信息结合，提升无人机搜索效率。
SLIDER: Sparse History-Guided Aerial Robot Target Search using Sliding Local Maps
- 用滑动局部地图+稀疏全局历史，避免依赖密集全局地图
- 实时评估观测质量，降低计算负载并加快决策速度
- 适合大规模未知环境下的实时无人机目标搜索
在大尺度未知环境中，空中机器人高效探索与目标搜索仍面临挑战，需兼顾广域覆盖、精细感知和实时决策。本文提出SLIDER框架，通过局部滑动地图与稀疏全局历史信息结合，避免依赖全局稠密地图。提出一种新型观测质量评估方法，利用历史位姿和传感器模型实时评估点云数据，实现高效前沿检测。为支持可扩展且响应迅速的规划，采用增量式视角聚类策略，动态适应局部更新，显著减少候选目标数量，降低计算负荷。同时，增量维护稀疏全局拓扑地图，辅助全局规划与代价评估。大量仿真与真实实验表明，该系统在内存占用、决策延迟和搜索效率方面优于现有先进方法。
原文摘要 · Abstract (English)
Efficient exploration and target search in large-scale unknown environments remain challenging for aerial robots due to the demands of broad spatial coverage, fine-grained perception, and real-time decision-making. This paper presents SLIDER, a lightweight and memory-efficient framework that avoids reliance on globally dense maps by combining a local sliding map with sparse global history information. A novel observation quality evaluation method is proposed, leveraging historical poses and sensor models to assess point cloud data in real-time, enabling efficient frontier detection. To support scalable and responsive planning, an incremental viewpoint clustering strategy dynamically adapts to local updates, significantly reducing the number of candidate targets and decreasing computational load. A sparse global topological map is incrementally maintained to assist global planning and cost evaluation. Extensive simulations and real-world experiments demonstrate that the proposed system outperforms state-of-the-art methods in memory usage, decision latency, and search efficiency.
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